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What’s behind the disparities in firm innovation rates across regions? Evidence on composition and context effects

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Abstract

Empirical evidence on innovation activity points to both significant disparities in innovation output across regions and important differences in firm innovation rates. These differences suggest that firm characteristics as well as regional factors might impact on innovation. Evidence on the relative importance of the two groups of factors is still scarce and ambiguous. We analyze the impact of firm characteristics and the regional context on differences in firm innovation rates in Germany for the period 1998–2009. By combining firm-level data with information on the regional environment, we can distinguish between composition effects caused by the selection of highly innovative firms in specific regions and the impact of regional factors. Our results indicate that the propensity to innovate of firms located in agglomerations significantly exceeds the innovation output of plants in rural regions. To analyze the role of the regional context for the firm’s probability to innovate, we use a multilevel approach. Besides controlling for important firm-level factors such as R&D employment, size and age of the firm, we also account for different regional factors. The regression results point to a positive association between regional R&D activity and the firm’s innovation output. Moreover, the effect of the regional context seems to differ with the size, age and R&D intensity of the firms.

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Notes

  1. We cannot provide a detailed review of the studies that investigate firm-level factors and refer to Patel and Pavitt (1995) and Cohen (2010) for comprehensive surveys.

  2. Johansson and Lööf (2008) capture the impact of the regional context by including region dummies. They interpret the positive effect of being located in the Stockholm area as evidence in favor of local technology spillovers and an important role of knowledge- intensive labor.

  3. Unfortunately, it is not possible to identify whether different establishments belong to the same firm. In the following, we use the terms firms and plants as synonyms for the term establishments to improve the readability.

  4. A detailed description of the IAB Establishment Panel can be found in Ellguth et al. (2014).

  5. From 2008 on, the question refers only to the previous year.

  6. The categorization is taken from the German Federal Institute for Research on Building, Urban Affairs and Spatial Development. Among others, it bases on the population density and the share of population that lives in larger cities. Agglomerated regions show the highest population density and a population share of minimum 50 % living in larger cities. Urbanized regions have a lower population density than agglomerations and a share of at least 33 % living in larger cities.

  7. The establishments report on innovations refers to the past 2 years, and from 2008 onwards, the corresponding question refers only to the previous year. The time lag of the explanatory variables takes this definition of the dependent variable into account.

  8. Regarding the graduates, we only consider disciplines that are supposed to be of specific importance for innovation activity: mathematics, natural science, engineering and technical science.

  9. See Van Oort et al. (2012) for a corresponding interpretation of sectors.

  10. In fact, Corrado and Fingleton (2011) argue that multilevel modeling and spatial econometrics have been rather unrelated methodological approaches. They try to connect the two literatures.

  11. See Van Oort et al. (2012) for similar arguments with respect to survival and growth of new establishments.

  12. Backman (2014) provides similar evidence for firm productivity in Sweden. Her results indicate that only 1.4 % of the variance of productivity can be assigned to the municipal level (see López-Bazo and Motellón (2013) for corresponding results for firm-level innovation in Spain).

  13. We have also estimated all specifications as logit models. The result of the linear and the logit models is more or less the same. The corresponding results are available from the authors upon request.

  14. Pfeifer and Wagner (2014) note that according to the corresponding literature one should expect a negative correlation between innovation and age because age is supposed to have a negative effect on human capital investments, cognitive skills such as reasoning, creativity, and fluid problem-solving skills which are important for innovative activities. When we use the mean age of the entire workforce instead of the age of the R&D workers, our results confirm the negative effect determined in previous studies. However, we believe that concerning the impact of age on innovation, it is more adequate to consider the age of those workers involved in R&D activities.

  15. The R&D share is highly correlated with the percentage of high-skilled workers, and the effect of R&D becomes significant if we exclude the share of high-skilled workers from the model. Thus, regarding the generation of new products and services, human capital seems to be more important than specialized R&D staff. In this context, we have to bear in mind that the majority of the establishments in our sample (75 %) does not employ R&D workers.

  16. For some firms, a very high turnover is reported (see Table 1). If we exclude the 1 % of firms with the highest turnover, i.e., with a turnover larger than 0.65, the significant negative effect vanishes. However, the results of the other variables are not affected. The corresponding results are available from the authors upon request.

  17. As reference region, we use the remote and rural region Vorpommern which shows the lowest average innovation rate of all functional regions.

  18. In order to differentiate clearly between firm- and regional-level effects and ensure exogeneity, we calculate the regional human capital net of the respective firm, i.e., we deduct the number of high-skilled employees of the firm under consideration from regional employment of high skilled. The same procedure is applied to R&D employment.

  19. We thank an anonymous referee for pointing this out. In an alternative specification, we include the spatial lag of graduates to address their mobility. The estimated coefficient is positive but not significantly different from zero. The result is available from the authors upon request.

  20. Firm-level results are available from the authors upon request.

  21. We are grateful to an anonymous referee for hinting at this important point.

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Acknowledgments

We gratefully thank three anonymous referees, Torben Dall Schmidt and seminar participants at the University of Barcelona, the Uddevalla Symposium 2014, the GfR Summer Conference in Regional Science 2014 in Marburg, and the ERSA Congress 2014 in St. Petersburg for their helpful remarks and suggestions.

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Authors and Affiliations

  1. Department of Border Regions Studies, University of Southern Denmark, Alsion 2, 6400, Sonderborg, Denmark

    Amber Naz

  2. IAB Nord, Regional Research Network of the Institute for Employment Research, Projensdorfer Str. 82, 24106, Kiel, Germany

    Annekatrin Niebuhr & Jan Cornelius Peters

  3. Empirical Labor Economics and Spatial Econometrics, Department of Economics, Christian-Albrechts-Universität zu Kiel, Olshausenstr. 40, 24098, Kiel, Germany

    Annekatrin Niebuhr

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  1. Amber Naz
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Correspondence to Amber Naz.

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Naz, A., Niebuhr, A. & Peters, J.C. What’s behind the disparities in firm innovation rates across regions? Evidence on composition and context effects. Ann Reg Sci 55, 131–156 (2015). https://doi.org/10.1007/s00168-015-0694-9

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  • DOI: https://doi.org/10.1007/s00168-015-0694-9

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